{
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  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T08:36:58.806283Z",
     "start_time": "2025-11-04T08:36:54.096991Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import torch.nn as nn\n"
   ],
   "id": "3ff42a1812e69559",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T03:30:30.891789Z",
     "start_time": "2025-11-04T03:30:30.832126Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# 在你的trainreason.ipynb中添加以下代码来创建示例数据：\n",
    "\n",
    "# 定义词汇表大小和特殊标记\n",
    "src_vocab_size = 10000\n",
    "trg_vocab_size = 8000\n",
    "BOS_TOKEN_IDX = 1  # 在你的Transformer中padding_idx=1，所以BOS用其他值\n",
    "EOS_TOKEN_IDX = 2\n",
    "PAD_TOKEN_IDX = 0\n",
    "\n",
    "# 创建随机示例数据\n",
    "import numpy as np\n",
    "\n",
    "def create_sample_data(num_samples=1000, max_length=20):\n",
    "    src_data = []\n",
    "    trg_data = []\n",
    "\n",
    "    for _ in range(num_samples):\n",
    "        # 随机生成源序列和目标序列长度(5-15个tokens)\n",
    "        src_len = np.random.randint(5, 16)\n",
    "        trg_len = np.random.randint(5, 16)\n",
    "\n",
    "        # 生成源序列 [BOS, tokens..., EOS]\n",
    "        src_seq = [BOS_TOKEN_IDX] + \\\n",
    "                  list(np.random.randint(3, src_vocab_size, src_len-2)) + \\\n",
    "                  [EOS_TOKEN_IDX]\n",
    "\n",
    "        # 生成目标序列 [BOS, tokens..., EOS]\n",
    "        trg_seq = [BOS_TOKEN_IDX] + \\\n",
    "                  list(np.random.randint(3, trg_vocab_size, trg_len-2)) + \\\n",
    "                  [EOS_TOKEN_IDX]\n",
    "\n",
    "        # 填充到固定长度\n",
    "        src_seq += [PAD_TOKEN_IDX] * (max_length - len(src_seq))\n",
    "        trg_seq += [PAD_TOKEN_IDX] * (max_length - len(trg_seq))\n",
    "\n",
    "        src_data.append(src_seq)\n",
    "        trg_data.append(trg_seq)\n",
    "\n",
    "    return src_data, trg_data\n",
    "\n",
    "# 生成示例训练数据\n",
    "# src_train_data, trg_train_data = create_sample_data(num_samples=1000, max_length=20)\n"
   ],
   "id": "7eeae6e3d585f972",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T08:27:10.611870Z",
     "start_time": "2025-11-04T08:26:55.529687Z"
    }
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   "cell_type": "code",
   "source": [
    "##########\n",
    "# 第 1 种数据集，机器翻译数据集\n",
    "##########\n",
    "# WMT系列数据集（广泛用于机器翻译任务）\n",
    "import torchtext.datasets as datasets\n",
    "\n",
    "# 示例：下载WMT14英语-德语数据集\n",
    "def load_wmt_data():\n",
    "    # 注意：这需要网络连接并且数据集较大\n",
    "    train_iter = datasets.WMT14(language_pair=('en', 'de'))\n",
    "    return train_iter\n",
    "\n",
    "# IWSLT数据集（口语翻译）\n",
    "def load_iwslt_data():\n",
    "    # 例如：英语到法语\n",
    "    train_iter = datasets.IWSLT2016(language_pair=('en', 'fr'))\n",
    "    return train_iter\n",
    "\n",
    "\n",
    "\n",
    "##########\n",
    "# 第 2 种数据集，文本摘要数据集\n",
    "##########\n",
    "# CNN/Daily Mail数据集（用于文本摘要）\n",
    "def load_cnn_daily_mail():\n",
    "    from datasets import load_dataset\n",
    "    dataset = load_dataset(\"cnn_dailymail\", version=\"3.0.0\")\n",
    "    return dataset\n",
    "\n",
    "\n",
    "##########\n",
    "# 第 3 种数据集，问答数据集\n",
    "##########\n",
    "\n",
    "\n",
    "# SQuAD数据集（阅读理解）\n",
    "def load_squad_data():\n",
    "    from datasets import load_dataset\n",
    "    squad_dataset = load_dataset(\"squad\")\n",
    "    return squad_dataset\n",
    "\n",
    "\n",
    "##########\n",
    "# 第 4 种数据集，通用语言建模数据集\n",
    "##########\n",
    "\n",
    "# WikiText数据集\n",
    "def load_wikitext():\n",
    "    from datasets import load_dataset\n",
    "    dataset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\n",
    "    return dataset\n",
    "\n",
    "# OpenWebText数据集（类似GPT训练数据）\n",
    "def load_openwebtext():\n",
    "    from datasets import load_dataset\n",
    "    dataset = load_dataset(\"openwebtext\")\n",
    "    return dataset\n",
    "\n",
    "\n"
   ],
   "id": "3abbf8d80f6e3f99",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T09:57:35.235832Z",
     "start_time": "2025-11-04T09:57:22.390715Z"
    }
   },
   "cell_type": "code",
   "source": [
    "'''\n",
    "修改数据加载代码以使用真实数据\n",
    "'''\n",
    "\n",
    "# 如果需要使用Hugging Face的tokenizer，可以这样实现：\n",
    "def create_real_data_with_hf_tokenizer(num_samples, max_length):\n",
    "    try:\n",
    "        from transformers import AutoTokenizer\n",
    "        from datasets import load_dataset\n",
    "\n",
    "        # 使用预训练的tokenizer（例如BERT tokenizer）\n",
    "        # 注意：这需要安装transformers库: pip install transformers\n",
    "        tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
    "\n",
    "        # 加载数据集\n",
    "        dataset = load_dataset(\"opus_books\", \"en-fr\", split='train')\n",
    "\n",
    "        src_data = []\n",
    "        trg_data = []\n",
    "\n",
    "        for i, example in enumerate(dataset):\n",
    "            if i >= num_samples:\n",
    "                break\n",
    "\n",
    "            # 获取文本内容\n",
    "            src_text = example['translation']['en']\n",
    "            trg_text = example['translation']['fr']\n",
    "\n",
    "            # Tokenization\n",
    "            src_tokens = tokenizer.encode(src_text, add_special_tokens=False)[:max_length-2]\n",
    "            trg_tokens = tokenizer.encode(trg_text, add_special_tokens=False)[:max_length-2]\n",
    "\n",
    "            # 添加特殊标记\n",
    "            src_seq = [tokenizer.cls_token_id or 1] + src_tokens + [tokenizer.sep_token_id or 2]\n",
    "            trg_seq = [1] + trg_tokens + [2]  # 使用默认的BOS/EOS ID\n",
    "\n",
    "            # 填充\n",
    "            src_seq += [tokenizer.pad_token_id or 0] * (max_length - len(src_seq))\n",
    "            trg_seq += [0] * (max_length - len(trg_seq))\n",
    "\n",
    "            src_data.append(src_seq)\n",
    "            trg_data.append(trg_seq)\n",
    "\n",
    "        return src_data, trg_data\n",
    "    except Exception as e:\n",
    "        print(f\"无法加载真实数据集或tokenizer，使用示例数据: {e}\")\n",
    "        return create_sample_data(num_samples, max_length)\n",
    "\n",
    "# 生成示例训练数据\n",
    "src_train_data, trg_train_data = create_real_data_with_hf_tokenizer(num_samples=10000, max_length=20)"
   ],
   "id": "941a8d22154587",
   "outputs": [],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T09:57:48.719707Z",
     "start_time": "2025-11-04T09:57:48.714070Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(len(src_train_data), len(trg_train_data))\n",
    "print(src_train_data[0], trg_train_data[0])\n",
    "print(len(src_train_data[0]), len(trg_train_data[0]))"
   ],
   "id": "f588e1811651d010",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 10000\n",
      "[101, 1996, 17677, 2121, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] [1, 3393, 2882, 2033, 4887, 19666, 2229, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "20 20\n"
     ]
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-11-04T09:57:54.717740Z",
     "start_time": "2025-11-04T09:57:54.700660Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 示例数据加载器\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "class TranslationDataset(Dataset):\n",
    "    def __init__(self, src_data, trg_data):\n",
    "        self.src_data = src_data\n",
    "        self.trg_data = trg_data\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src_data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return {\n",
    "            'src': torch.tensor(self.src_data[idx], dtype=torch.long),\n",
    "            'trg': torch.tensor(self.trg_data[idx], dtype=torch.long)\n",
    "        }\n",
    "\n",
    "# 创建数据加载器\n",
    "train_dataset = TranslationDataset(src_train_data, trg_train_data)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)"
   ],
   "id": "initial_id",
   "outputs": [],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-05T04:47:01.432860Z",
     "start_time": "2025-11-05T00:36:32.944393Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\"\"\"\n",
    "训练\n",
    "\"\"\"\n",
    "from transformer_source.my.Transformer import Transformer\n",
    "from transformers import AutoTokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
    "src_vocab_size = tokenizer.vocab_size\n",
    "trg_vocab_size = tokenizer.vocab_size\n",
    "# 设置设备\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# 初始化模型\n",
    "model = Transformer(\n",
    "    src_pad_ix=0,\n",
    "    trg_pad_ix=0,\n",
    "    enc_voc_size=src_vocab_size,\n",
    "    dec_voc_size=trg_vocab_size,\n",
    "    d_model=512,\n",
    "    max_len=512,\n",
    "    n_heads=8,\n",
    "    ffn_hidden=2048,\n",
    "    n_layers=6,\n",
    "    drop_prod=0.1,\n",
    "    device=device\n",
    ").to(device)\n",
    "\n",
    "# 使用交叉熵损失，忽略填充位置\n",
    "criterion = nn.CrossEntropyLoss(ignore_index=0)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)\n",
    "\n",
    "def train_epoch(model, data_loader, optimizer, criterion, device):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "\n",
    "    for batch_idx, batch in enumerate(data_loader):\n",
    "        src = batch['src'].to(device)\n",
    "        trg = batch['trg'].to(device)\n",
    "\n",
    "        # 目标序列需要移位一位作为输入\n",
    "        trg_input = trg[:, :-1]\n",
    "        targets = trg[:, 1:]\n",
    "\n",
    "        # 前向传播\n",
    "        logits = model(src, trg_input)\n",
    "\n",
    "        # 计算损失\n",
    "        loss = criterion(logits.reshape(-1, logits.shape[-1]), targets.reshape(-1))\n",
    "\n",
    "        # 反向传播\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        total_loss += loss.item()\n",
    "\n",
    "        if batch_idx % 100 == 0:\n",
    "            print(f\"Batch {batch_idx}, Loss: {loss.item():.4f}\")\n",
    "\n",
    "    return total_loss / len(data_loader)\n",
    "\n",
    "# 初始化最佳损失值\n",
    "best_loss = float('inf')\n",
    "\n",
    "# 保存检查点函数\n",
    "def save_checkpoint(model, optimizer, epoch, loss, filepath):\n",
    "    checkpoint = {\n",
    "        'epoch': epoch,\n",
    "        'model_state_dict': model.state_dict(),\n",
    "        'optimizer_state_dict': optimizer.state_dict(),\n",
    "        'loss': loss,\n",
    "        'src_vocab_size': src_vocab_size,\n",
    "        'trg_vocab_size': trg_vocab_size\n",
    "    }\n",
    "    torch.save(checkpoint, filepath)\n",
    "    print(f\"Model saved to {filepath}\")\n",
    "\n",
    "# 训练多个epoch\n",
    "num_epochs = 40\n",
    "for epoch in range(num_epochs):\n",
    "    avg_loss = train_epoch(model, train_loader, optimizer, criterion, device)\n",
    "    print(f\"Epoch [{epoch+1}/{num_epochs}], Average Loss: {avg_loss:.4f}\")\n",
    "\n",
    "     # 保存最佳模型\n",
    "    if avg_loss < best_loss:\n",
    "        best_loss = avg_loss\n",
    "        save_checkpoint(\n",
    "            model,\n",
    "            optimizer,\n",
    "            epoch,\n",
    "            avg_loss,\n",
    "            'transformer_best_model.pth'\n",
    "        )\n",
    "        print(f\"New best model saved with loss: {best_loss:.4f}\")"
   ],
   "id": "67a7a0758260e948",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch 0, Loss: 10.5110\n",
      "Batch 100, Loss: 5.1995\n",
      "Batch 200, Loss: 4.5399\n",
      "Batch 300, Loss: 4.3291\n",
      "Epoch [1/20], Average Loss: 5.3709\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 5.3709\n",
      "Batch 0, Loss: 4.1080\n",
      "Batch 100, Loss: 4.0291\n",
      "Batch 200, Loss: 3.6999\n",
      "Batch 300, Loss: 3.6067\n",
      "Epoch [2/20], Average Loss: 3.8571\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 3.8571\n",
      "Batch 0, Loss: 3.5645\n",
      "Batch 100, Loss: 3.1732\n",
      "Batch 200, Loss: 3.1932\n",
      "Batch 300, Loss: 3.3628\n",
      "Epoch [3/20], Average Loss: 3.3998\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 3.3998\n",
      "Batch 0, Loss: 3.2181\n",
      "Batch 100, Loss: 3.2793\n",
      "Batch 200, Loss: 3.1096\n",
      "Batch 300, Loss: 2.9713\n",
      "Epoch [4/20], Average Loss: 3.1073\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 3.1073\n",
      "Batch 0, Loss: 2.8090\n",
      "Batch 100, Loss: 2.6467\n",
      "Batch 200, Loss: 2.9369\n",
      "Batch 300, Loss: 2.9482\n",
      "Epoch [5/20], Average Loss: 2.8772\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 2.8772\n",
      "Batch 0, Loss: 2.9700\n",
      "Batch 100, Loss: 2.6665\n",
      "Batch 200, Loss: 2.6521\n",
      "Batch 300, Loss: 2.5110\n",
      "Epoch [6/20], Average Loss: 2.6886\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 2.6886\n",
      "Batch 0, Loss: 2.5984\n",
      "Batch 100, Loss: 2.4651\n",
      "Batch 200, Loss: 2.7142\n",
      "Batch 300, Loss: 2.4718\n",
      "Epoch [7/20], Average Loss: 2.5127\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 2.5127\n",
      "Batch 0, Loss: 2.4290\n",
      "Batch 100, Loss: 2.4241\n",
      "Batch 200, Loss: 2.0964\n",
      "Batch 300, Loss: 2.3384\n",
      "Epoch [8/20], Average Loss: 2.3401\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 2.3401\n",
      "Batch 0, Loss: 2.2893\n",
      "Batch 100, Loss: 2.1963\n",
      "Batch 200, Loss: 2.2573\n",
      "Batch 300, Loss: 2.2147\n",
      "Epoch [9/20], Average Loss: 2.1738\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 2.1738\n",
      "Batch 0, Loss: 1.9784\n",
      "Batch 100, Loss: 1.9899\n",
      "Batch 200, Loss: 2.0562\n",
      "Batch 300, Loss: 2.0839\n",
      "Epoch [10/20], Average Loss: 2.0064\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 2.0064\n",
      "Batch 0, Loss: 1.8117\n",
      "Batch 100, Loss: 1.9129\n",
      "Batch 200, Loss: 1.8413\n",
      "Batch 300, Loss: 2.0168\n",
      "Epoch [11/20], Average Loss: 1.8424\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 1.8424\n",
      "Batch 0, Loss: 1.6165\n",
      "Batch 100, Loss: 1.7725\n",
      "Batch 200, Loss: 1.7495\n",
      "Batch 300, Loss: 1.7434\n",
      "Epoch [12/20], Average Loss: 1.6789\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 1.6789\n",
      "Batch 0, Loss: 1.4246\n",
      "Batch 100, Loss: 1.5024\n",
      "Batch 200, Loss: 1.5119\n",
      "Batch 300, Loss: 1.5000\n",
      "Epoch [13/20], Average Loss: 1.5186\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 1.5186\n",
      "Batch 0, Loss: 1.3365\n",
      "Batch 100, Loss: 1.4414\n",
      "Batch 200, Loss: 1.4398\n",
      "Batch 300, Loss: 1.4283\n",
      "Epoch [14/20], Average Loss: 1.3610\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 1.3610\n",
      "Batch 0, Loss: 1.1106\n",
      "Batch 100, Loss: 1.2745\n",
      "Batch 200, Loss: 1.3324\n",
      "Batch 300, Loss: 1.3064\n",
      "Epoch [15/20], Average Loss: 1.2151\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 1.2151\n",
      "Batch 0, Loss: 1.0068\n",
      "Batch 100, Loss: 1.0696\n",
      "Batch 200, Loss: 1.1237\n",
      "Batch 300, Loss: 1.1730\n",
      "Epoch [16/20], Average Loss: 1.0734\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 1.0734\n",
      "Batch 0, Loss: 0.8814\n",
      "Batch 100, Loss: 0.9818\n",
      "Batch 200, Loss: 0.9325\n",
      "Batch 300, Loss: 1.0747\n",
      "Epoch [17/20], Average Loss: 0.9377\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 0.9377\n",
      "Batch 0, Loss: 0.7704\n",
      "Batch 100, Loss: 0.8052\n",
      "Batch 200, Loss: 0.8267\n",
      "Batch 300, Loss: 0.7630\n",
      "Epoch [18/20], Average Loss: 0.8154\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 0.8154\n",
      "Batch 0, Loss: 0.7165\n",
      "Batch 100, Loss: 0.5814\n",
      "Batch 200, Loss: 0.6977\n",
      "Batch 300, Loss: 0.6659\n",
      "Epoch [19/20], Average Loss: 0.7047\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 0.7047\n",
      "Batch 0, Loss: 0.4478\n",
      "Batch 100, Loss: 0.5798\n",
      "Batch 200, Loss: 0.6940\n",
      "Batch 300, Loss: 0.6393\n",
      "Epoch [20/20], Average Loss: 0.6054\n",
      "Model saved to transformer_best_model.pth\n",
      "New best model saved with loss: 0.6054\n"
     ]
    }
   ],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-05T05:38:43.188438Z",
     "start_time": "2025-11-05T05:37:15.294700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from transformers import AutoTokenizer\n",
    "from transformer_source.my.Transformer import Transformer\n",
    "\n",
    "# 设置设备\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# 加载预训练的tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
    "src_vocab_size = tokenizer.vocab_size\n",
    "trg_vocab_size = tokenizer.vocab_size\n",
    "\n",
    "# 初始化模型\n",
    "model = Transformer(\n",
    "    src_pad_ix=0,\n",
    "    trg_pad_ix=0,\n",
    "    enc_voc_size=src_vocab_size,\n",
    "    dec_voc_size=trg_vocab_size,\n",
    "    d_model=512,\n",
    "    max_len=512,\n",
    "    n_heads=8,\n",
    "    ffn_hidden=2048,\n",
    "    n_layers=6,\n",
    "    drop_prod=0.1,\n",
    "    device=device\n",
    ").to(device)\n",
    "\n",
    "# 加载训练好的模型权重\n",
    "checkpoint = torch.load('transformer_best_model.pth', map_location=device)\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "model.eval()\n",
    "\n",
    "# 定义特殊标记\n",
    "BOS_TOKEN_IDX = 1\n",
    "EOS_TOKEN_IDX = 2\n",
    "PAD_TOKEN_IDX = 0\n",
    "\n",
    "def prepare_input_sequence(text, max_length=20):\n",
    "    \"\"\"\n",
    "    准备输入序列\n",
    "    text: 英文文本字符串\n",
    "    \"\"\"\n",
    "    # 使用tokenizer对输入文本进行编码\n",
    "    tokens = tokenizer.encode(text, add_special_tokens=False)[:max_length-2]\n",
    "\n",
    "    # 添加特殊标记\n",
    "    src_seq = [tokenizer.cls_token_id or BOS_TOKEN_IDX] + tokens + [tokenizer.sep_token_id or EOS_TOKEN_IDX]\n",
    "\n",
    "    # 填充到固定长度\n",
    "    src_seq += [tokenizer.pad_token_id or PAD_TOKEN_IDX] * (max_length - len(src_seq))\n",
    "\n",
    "    return torch.tensor([src_seq], dtype=torch.long).to(device)\n",
    "\n",
    "def translate_sentence(model, src_tensor, max_length=50):\n",
    "    \"\"\"\n",
    "    翻译句子\n",
    "    model: 训练好的模型\n",
    "    src_tensor: 编码后的源语言句子\n",
    "    max_length: 最大生成长度\n",
    "    \"\"\"\n",
    "    # 编码器前向传播\n",
    "    src_mask = model.make_src_mask(src_tensor)\n",
    "    enc_output = model.encoder(src_tensor, src_mask)\n",
    "\n",
    "    # 初始化目标序列，以BOS标记开始\n",
    "    trg_indices = [BOS_TOKEN_IDX]\n",
    "\n",
    "    for i in range(max_length):\n",
    "        # 构造目标张量\n",
    "        trg_tensor = torch.LongTensor(trg_indices).unsqueeze(0).to(src_tensor.device)\n",
    "\n",
    "        # 创建解码器掩码\n",
    "        trg_mask = model.make_pad_mask(trg_tensor, trg_tensor, model.trg_pad_ix, model.trg_pad_ix) * \\\n",
    "                   model.make_casual_mask(trg_tensor, trg_tensor)\n",
    "        src_cross_mask = model.make_src_mask(src_tensor)\n",
    "\n",
    "        # 解码器前向传播\n",
    "        output = model.decoder(trg_tensor, enc_output, trg_mask, src_cross_mask)\n",
    "\n",
    "        # 获取最后一个词的预测\n",
    "        pred_token = output.argmax(2)[:, -1].item()\n",
    "\n",
    "        # 避免重复生成相同token\n",
    "        if len(trg_indices) > 1 and pred_token == trg_indices[-1] and pred_token != EOS_TOKEN_IDX:\n",
    "            # 可以采用top-k采样或其他策略避免重复\n",
    "            pass\n",
    "        trg_indices.append(pred_token)\n",
    "\n",
    "        # 如果预测到结束符则停止\n",
    "        if pred_token == EOS_TOKEN_IDX:\n",
    "            break\n",
    "\n",
    "    return trg_indices\n",
    "\n",
    "def translate_text(english_text):\n",
    "    \"\"\"\n",
    "    将英文文本翻译成法文\n",
    "    english_text: 输入的英文文本\n",
    "    \"\"\"\n",
    "    # 准备输入\n",
    "    src_tensor = prepare_input_sequence(english_text)\n",
    "\n",
    "    # 执行翻译\n",
    "    translated_tokens = translate_sentence(model, src_tensor, max_length=50)\n",
    "\n",
    "    # 解码为文本\n",
    "    translated_text = tokenizer.decode(translated_tokens[1:-1], skip_special_tokens=True)\n",
    "\n",
    "    return translated_text\n",
    "\n",
    "# 使用示例\n",
    "if __name__ == \"__main__\":\n",
    "    # 测试翻译\n",
    "    english_sentences = [\n",
    "        \"Hello, how are you?\",\n",
    "        \"I love machine learning.\",\n",
    "        \"The weather is nice today.\"\n",
    "    ]\n",
    "\n",
    "    for sentence in english_sentences:\n",
    "        french_translation = translate_text(sentence)\n",
    "        print(f\"English: {sentence}\")\n",
    "        print(f\"French:  {french_translation}\")\n",
    "        print(\"-\" * 50)\n"
   ],
   "id": "7c769ba66edbeab2",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /bert-base-uncased/resolve/main/tokenizer_config.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1c717a8d0>, 'Connection to huggingface.co timed out. (connect timeout=10)'))\"), '(Request ID: 3f6d53e7-10e3-449e-a22d-a44970ee278c)')' thrown while requesting HEAD https://huggingface.co/bert-base-uncased/resolve/main/tokenizer_config.json\n",
      "Retrying in 1s [Retry 1/5].\n",
      "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /bert-base-uncased/resolve/main/tokenizer_config.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1c717b530>, 'Connection to huggingface.co timed out. (connect timeout=10)'))\"), '(Request ID: a49712ea-62dd-4b79-be8f-39df6619c10c)')' thrown while requesting HEAD https://huggingface.co/bert-base-uncased/resolve/main/tokenizer_config.json\n",
      "Retrying in 2s [Retry 2/5].\n",
      "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /bert-base-uncased/resolve/main/tokenizer_config.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1c717b560>, 'Connection to huggingface.co timed out. (connect timeout=10)'))\"), '(Request ID: 657033e7-811c-413f-abb7-1d31c76599a1)')' thrown while requesting HEAD https://huggingface.co/bert-base-uncased/resolve/main/tokenizer_config.json\n",
      "Retrying in 4s [Retry 3/5].\n",
      "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /bert-base-uncased/resolve/main/tokenizer_config.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1c717b440>, 'Connection to huggingface.co timed out. (connect timeout=10)'))\"), '(Request ID: f57644bb-95fc-47d2-92f5-0811afce0b29)')' thrown while requesting HEAD https://huggingface.co/bert-base-uncased/resolve/main/tokenizer_config.json\n",
      "Retrying in 8s [Retry 4/5].\n",
      "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /bert-base-uncased/resolve/main/tokenizer_config.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1c7178c80>, 'Connection to huggingface.co timed out. (connect timeout=10)'))\"), '(Request ID: 057929b8-70b0-402c-ad2a-96b7533b2427)')' thrown while requesting HEAD https://huggingface.co/bert-base-uncased/resolve/main/tokenizer_config.json\n",
      "Retrying in 8s [Retry 5/5].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "English: Hello, how are you?\n",
      "French:  avez - vous de savoir comment?\n",
      "--------------------------------------------------\n",
      "English: I love machine learning.\n",
      "French:  je m ’ etonne.\n",
      "--------------------------------------------------\n",
      "English: The weather is nice today.\n",
      "French:  la ferme est tombe.\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "execution_count": 41
  }
 ],
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